Cassie Kozyrkov
Staying Safe in the AI Future
#1about 6 minutes
Avoid science fiction and see AI as a tool
AI should be understood as a powerful tool for writing software, not as a person, to avoid common misconceptions.
#2about 2 minutes
Understand that AI objectives are fundamentally subjective
The correct output of an AI system is determined by its intended purpose, making the definition of success subjective.
#3about 4 minutes
Be careful what you ask for from AI systems
AI systems are reliable workers that execute instructions literally, so poorly defined objectives can lead to unintended and foolish outcomes.
#4about 3 minutes
Reinject thoughtfulness into simplified AI instructions
AI development simplifies coding to just an objective and a dataset, requiring developers to consciously add back the thoughtfulness that traditional coding demanded.
#5about 4 minutes
Adopt a reliability mindset and plan for mistakes
Expect AI systems to make mistakes and build in safety nets, adopting a site reliability engineering (SRE) approach to mitigate failures.
#6about 3 minutes
Test models on new data to avoid overfitting
Models can easily memorize training data, so you must test them on a separate, pristine dataset to ensure they have learned to generalize.
#7about 2 minutes
Look for spurious correlations beyond test accuracy
A model can achieve high accuracy by learning unintended patterns, like a background object, rather than the intended subject.
#8about 3 minutes
Treat datasets as textbooks reflecting human bias
Datasets are like textbooks created by humans and inevitably reflect the implicit values and biases of their authors.
#9about 1 minute
Embrace diversity as a requirement for safe AI
Building teams with diverse perspectives, backgrounds, and life experiences is a mandatory requirement for identifying and mitigating bias in AI systems.
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